Automated imaging and identification of proteoforms directly from ovarian cancer tissue

The molecular identification of tissue proteoforms by top-down mass spectrometry (TDMS) is significantly limited by throughput and dynamic range. We introduce AutoPiMS, a single-ion MS based multiplexed workflow for top-down tandem MS (MS2) directly from tissue microenvironments in a semi-automated manner. AutoPiMS directly off human ovarian cancer sections allowed for MS2 identification of 73 proteoforms up to 54 kDa at a rate of <1 min per proteoform. AutoPiMS is directly interfaced with multifaceted proteoform imaging MS data modalities for the identification of proteoform signatures in tumor and stromal regions in ovarian cancer biopsies. From a total of ~1000 proteoforms detected by region-of-interest label-free quantitation, we discover 303 differential proteoforms in stroma versus tumor from the same patient. 14 of the top proteoform signatures are corroborated by MSI at 20 micron resolution including the differential localization of methylated forms of CRIP1, indicating the importance of proteoform-enabled spatial biology in ovarian cancer.

methods section?This led to some inconsistencies and should be avoided.There are several different nanoDESI analyses included in this report, and AutoPiMS is only a part of the story.There should be a much clearer description of how these separate workflows play and integrate.(Also, no need to introduce abbreviation for Hertz.)Lines 101-109: 87 of 113 proteoforms, yet favorable windows only for 25? Are these 25 a subset of larger proteoforms within the dataset?Supplementary figure 4 suggests these proteoforms were detected with incredibly low SNR (MS1), which is concerning.Please comment.Line 110 (and elsewhere): Replace MS2 spectra with tandem MS or fragment spectra Line 118: Supplementary figure 6 seems to be out of context.It is unclear how this connects to regular MS2 and I2MS-MS2.What is the take home message?Line 119: 1% relative abundance of a noisy spectrum is not informative.SNR might be better performance metrics then relative abundance.Lines 117-127: Clarify which instruments are being used.One could assume Q Exactive for the "ensemble mode" but was it the Plus or the HF? Provide some explanation as why so many different instruments have been used as this could potentially limit the applicability.What is meant by technical replicates in this context?Also, comment on reproducibly among replicates.Line 152: Was AutoPiMS employed for LFQ?If so, how exactly was this accomplished?This information is not directly stated anywhere other than Figure 2a where it is unclear.This is important because of potential oversampling effects on downstream quantitation.Figures are overly complicated and therefore difficult to follow and connect to explanations and conclusions provided in the text.Some of this material can be moved to SI to make the overall flow of the experiment clearer and connect better to the text.For example, Figures 1c and 1d could be moved to SI without losing any context and same goes for graphical fragment maps.In this case, less is more.In Figure 2a, was AutoPiMS applied only along the vertical dashed line (and not implemented for the PiMS region)?How can one be certain that a monomethylation is present solely on Arg68 if only one AutoPiMS vertical line scan was applied?Terminology is also confusing, "pixels" would suggest that the LFQ experiment could create images.If so, why not include these images?It is challenging to gauge small differences in the images with Jet or other color schemes, consider changing to viridis or ciridis.Overlaying of Jet with a red-scale image makes figure 2f hard to read. Figure 2 and related text: Ideally, the report should have included a validation of selected proteins (and/or PTMs) using complementary techniques.
This study developed a novel technique, AutoPiMS, for the detection of spatial proteoforms in human tissues.This technique is impressive due to its ability to combine potentially several methods into one, enabling the characterization of proteoforms in a 4-dimensional manner.Importantly the technique provides an opportunity to delineate (with accuracy and speed) proteoforms associated with tumour vs stroma cells with potential applications in many other solid tumours especially those in which the interplay between tumour and stroma cells have significance in therapy and patient outcome.The article is well written, detailed and with novel findings and as such should be published.One minor comment is for the authors to consider briefly highlighting the (potential) clinical significance of the differential expression of CRIP1 in tumour regions.Some studies (such as https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8820892/)have alluded to this.

Reviewer #3 (Remarks to the Author)
The significance of this study lies in the development of an integrated platform, AutoPiMS, that drives advancements in proteoform-level spatial biology.The platform enables four-dimensional characterization of proteoform signatures: intact molecular mass, spatial distribution, quantitative analysis of differential expression, and molecular identification.In particular, the automated data acquisition engine enables proteoform identification up to ~54 kDa at a speed of <1 minute per proteoform directly off tissue.A spatially resolved study of ovarian cancer tissue with 20-micron resolution identified >300 differential proteoforms in stroma versus tumor regions from the same patient, including differential localization of methylated forms of CRIP1.This platform fills the gap between high-confidence proteoform discovery and spatial proteomics, opening up a new avenue for discoveries and precision diagnostics in clinical histology.I have a minor question.
Have the researchers compared their findings to existing methods or performed any validation?It would be nice to have a specific comparison of the advantages and disadvantages of the autoPiMS method compared to traditional immunohistochemistry results.

Reviewer 1
Excerpted General Comments:  (i.e., nanoDESI-MSI), label-free quantitation (LFQ), or AutoPiMS workflow.The latter is a novel and ambitious approach for high-quality spatially resolved MSn suitable for continuous flow ambient ionization probes such as nanoDESI, DESI, etc. that has a potential to reshape in-situ MSn for MSI applications if automated and made broadly applicable.Current implementation is however semi-automated, as it takes nanoDESI-MS1 outcome as input to generate MS method to acquire nanoDESI-MS2.Regardless, it represents an important step towards spatially resolved proteomics, and more specifically proteoform imaging, which is an essential tool in spatial biology toolkit.

Response:
We heartfully thank the reviewer for these positive comments, and for their time and care in evaluation of the work.We respond below with the general aspiration to match the energy with detailed, earnest revisions.

Detailed Comments:
1) The approach is technically sound and uses best-practice methods yielding high-quality data.
However, the writing lacks clarity, material presented is quite complex and difficult to follow.

Response:
We thank the Reviewer for the constructive feedback for the writing structure of the paper.As the Reviewer will find in this revision, the manuscript structure and the materials presented has been carefully thought through and revised to best reflect clarity according to the reviewer's suggestions.In particular, we have rewritten the Abstract (147 words), expanded the introduction paragraphs to introduce AutoPiMS as a novel spatially-resolved on-tissue proteoform identification workflow (Line 50-69), reformulated the walkthrough paragraph of the AutoPiMS workflow (Line 134-147), clarified the relationship among datatypes presented for the AutoPiMS-enabled spatial proteoform biology study .The Reviewer will find detailed changes in the track-change version of the manuscript and the Responses to specific questions in this letter in the following.
2) The abstract would suggest high-resolution images of 1000 proteoforms were produced, which is misleading.

Response:
We indeed detected circa 1000 proteoforms by label-free quantitation from the human ovarian cancer tissues studied by PiMS.We have revised the Abstract to clearly define the differences among proteoforms 'detected', 'identified' and found to be 'differentially expressed' (by label-free quantitation), what we believe to be the source of the issues and comments about the putatively misleading passages.Here is what we have put forward in the revised Abstract and aligned the manuscript content with this change (Line 34): "From a total of ~1000 proteoforms detected by region-of-interest label-free quantitation, we discovered 303 differential proteoforms in stroma versus tumor from the same patient." 3) The overall workflow can be broken into three major areas: (1) traditional MSI with single acquisition per pixel at ~20 µm by 80 µm resolution, (2) spatial label free quantitation (LFQ) with ~50 acquisitions per pixel at 80 µm by 80 µm resolution, and (3) AutoPiMS, which completes a MS1 survey scan (either in normal mode or I2MS mode) followed by MS2 (either in real-time or after some level of post-processing, this is unclear).The authors should rewrite the abstract and all relevant sections for clarity and explain how PiMS, LFQ, and AutoPiMS workflows intersect and interact.Including a schematic early on would be effective as it would help frame the reader's expectations and clarify where newly introduced AutoPiMS is used and how it is implemented.Below are several comments directed to each of the three major areas of this report.
Response: We would like to make the clarification that AutoPiMS refers to the newly-developed workflow for on-tissue multiplexed MS 2 to identify and characterize intact proteoforms.These proteoforms were discovered from multifaceted PiMS-derived datatypes including full MS 1 profile, proteoform images, and label-free quantitation (LFQ).As the reviewer pointed out in their summary, we have revised the manuscript systematically to make these clarifications.In particular, we have rewritten the Abstract to clarify the focus on AutoPiMS as an on-tissue multiplexed MS 2 workflow.Moreover, we kept the focus of Fig. 1

Response:
We have added a sentence in the Methods section to clarify that I 2 MS detection was enabled on the Exploris system: "The Orbitrap mass analyzer on Exploris 480 system operates at a central electrode voltage of 4 kV, allowing for more favorable ion lifetimes for I 2 MS over models that operate at 5 kV."To offset the complexities from the use of multiple instruments, we have reorganized and rewritten the Methods section to improve the clarity for each workflow discussed in the manuscript.Now, the revised Methods section is composed of four main sections: PiMS imaging ("PiMS ion source and sampling conditions" and "PiMS imaging data acquisition and processing"), label-free quantitation ("Label-free quantitation (LFQ)"), AutoPiMS ("AutoPiMS algorithms", "AutoPiMS data acquisition", and "Database search for AutoPiMS"), and "Intact Mass Tag (IMT) Search & Gene Ontology (GO) analysis".In the AutoPiMS section, we start from introducing the algorithms of how the MS/MS methods are generated followed by detailed MS/MS data acquisition for direct infusion mixture, <17 kDa proteoforms on tissue, and >17 kDa proteoforms directly from tissue.400 proteoforms were detected on the Q Exactive Plus in earlier report.How many were detected on the Exploris?Given greater ion flux, one would expect higher sensitivity, which should translate into greater depth of coverage.If I 2 MS was employed (and it is unclear if it was), the benefit should have been even greater.
Response: In this imaging dataset, we detected 618 proteoforms above 0.1% relative abundance (compared to ~400 observed in the earlier report from Su et al., Sci. Adv., 2022).To address the reviewer's question, we revised the manuscript (Line 200) and added a full spectrum and a list of 618 proteoform mass values detected in this imaging dataset: "In this imaging dataset, 618 proteoforms were detected and imaged above 0.1% relative abundance (Supplementary Table 14 and Supplementary Fig. 10)."We have also clarified in the Methods section that I 2 MS was employed for this imaging experiment as mentioned in the response above.
3)-2 The LFQ experiment, which is not an MSI experiment, represents a major outcome described in this report (with >1000 proteoforms).However, a less-than-careful reader might easily come away believing these are all MSI results and MSI produced 1000 images in the traditional PiMS experiment, which is clearly not the case.In reality images were obtained for much smaller number of proteoforms.
Response: As noted above, we agree with the Reviewer on the confusion between LFQ and imaging results.Given the multiple areas of advancement within this manuscript, we have moved to clarify the workflows and datasets emerging from them.So, in the revised manuscript, we report clearly that PiMS imaging of the different regions of interest on the same tissue section, from the same patient, resulted in the detection and imaging of a total of 618 proteoform masses (Line 199): "In the next step, we compared the LFQ results with a PiMS imaging experiment performed on a region with spatially comingled tumor and stromal compositions (Fig. 2a).In this imaging dataset, 618 proteoforms were detected above 0.1% relative abundance (Supplementary Table 14 and Supplementary Fig. 10)." Similarly, the first impression is that the Figure 1 line scan workflow was done for everything, hence the name "AutoPiMS", but apparently this is not the case.The inclusion of the MSI data is also confusing.Given the focus on LFQ of the 1013 features, why choose just these 17 images?
Response: We agree with the Reviewer and revised the manuscript to better explain the relationship between the samples and the panels in the figures.We used the first patient sample (~95% tumor) to demonstrate AutoPiMS as a multiplexed on-tissue MS/MS workflow to identify proteoforms, and all display items in Fig. 1 were obtained from that first patient sample.In Fig. 2, we moved on to the second patient sample of higher complexity (comingled tumor and stroma regions).For this complex sample, we employed the set of PiMS-related workflows to understand its spatial biology, including imaging, LFQ to assert differential proteoform expression and MS/MS identification using AutoPiMS.AutoPiMS here was employed to specifically identify and characterize the 17 proteoform signatures with images displayed in Fig. 2b that are differentially detected in tumor and stromal regions.Despite 1013 proteoforms detected in LFQ, some of these proteoforms did not contain enough ion counts to generate an informative, quality image or did not show distinct features in their PiMS images.Moreover, the imaging experiment was performed on a different region of the same tissue compared to LFQ, which may contain a different set of proteoforms due to the spatial heterogeneity.We have revised the first sentence describing Fig. 2 to make the clarification as follows (Line 177): "We deployed AutoPiMS to identify proteoform signatures in histology-defined tumor versus stromal regions within HGSOC tissue from a single patient."Also these changes on Line 199 and Line 209 were also made, respectively: "In the next step, we compared the LFQ results with a PiMS imaging experiment performed on a region with spatially comingled tumor and stromal compositions (Fig. 2a).In this imaging dataset, 618 proteoforms were detected above 0.1% relative abundance (Supplementary Table 14 and Supplementary Fig. 10)... Fig. 2b shows PiMS images of 17 of the proteoforms with highly differential ion counts between tumor and stromal regions in LFQ." (Line 209): "AutoPiMS was subsequently employed for direct top-down MS/MS of these proteoform signatures...All 17 proteoforms were detected in the survey scan, 16 of them were MS 1 annotated, and 14 of them were MS 2 identified (Supplementary Table 15)."How many features were annotated in AutoPiMS that were detected in PiMS, and furthermore, how many were detected in AutoPiMS survey scans that were not detected with PiMS?
Response: We reasoned that the Reviewer was asking particularly about the results shown in Fig. 2 as a follow up to the previous comment.As explained in the previous response, we used AutoPiMS to selectively target the 17 proteoforms with PiMS images shown in Fig. 2b.In this experiment, 14 of the 17 PiMS-detected proteoforms were identified by AutoPiMS.All proteoforms detected in AutoPiMS survey scan were detected in PiMS.To further clarify, we have also added a table of the information for the 17 proteoforms as Supplementary Table 15 in the revised package.The particular part of the manuscript revised to address the Reviewer's comments is as follows (Line 209): "AutoPiMS was subsequently employed for direct top-down MS 2 of these 17 signature proteoforms...All 17 proteoforms were detected in the survey scan, 16 of them were MS 1 annotated, and 14 of them were MS 2 identified (Supplementary Table 15)."

Any mention of near cellular resolution has the caveat that the width of the pixel is 80 µm, which can easily contain several if not over a dozen cells. Any mention of resolution should
clearly state that it is not a square pixel, but it is ~20 µm by 80 µm pixel, and even then, it is variable in nanoDESI experiment.

Response:
We agree with the Reviewer that the line spacing of 80 µm should be specified in the manuscript when 20 µm lateral spatial resolution is mentioned.We have revised the manuscript accordingly (Line 236): "Given the ~20 micron lateral spatial resolution and 80 micron line spacing, cell-specific observations as well as the functional role of Arg68me0 in angiogenesis will require future study."

3)-2 Spatial label free quantitation (LFQ):
Averaging ~50 acquisitions per "pixel" for LFQ is interesting terminology as for this to be a "pixel" there should be images generated from this analysis.While the broader area MSI was included, no images were presented from the LFQ workflow.This is a comparable level of sampling to laser capture microdissection (LCM) based approaches and images have been produced from LCM-LCMS, why not here?
Response: We agree with the Reviewer that "pixel" is a terminology indicating images are generated.LFQ experiments were performed in a similar way as the PiMS imaging experiments, which has been described in the revised manuscript and Methods section ("Label-free quantitation (LFQ)"), and will be explained in further responses to two Reviewer questions below.In contrast to the PiMS imaging experiments performed in complex tissue regions, LFQ experiments were designed to take hundreds of samples from well-defined tumor or stromal regions to construct the statistics for quantitative interpretations.Therefore, the 240 "pixels" in LFQ experiments highlighted in Fig. 2a did not intend to demonstrate the spatial differences in the pathological context (e.g., tumor, stroma).Moreover, with the spatial resolution of up to 20 µm, we do not assert any cell type or intratumor heterogeneities in this piece.
To conclude, we agree with the Reviewer that proteoform images can be generated from the LFQ dataset similar to laser capture microdissection coupled to LC-MS workflows, but we do not show LFQ-derived proteoform images in this particular workflow.To address the reviewer's concern about the terminology "pixel" used in the paper, we used "sampled region" instead in the revised manuscript for clarification.The Reviewer will find these changes in Fig. 2a and in Line 157, 183, and 187 in the Fig. 2 captions and the manuscript.Moreover, we updated the LFQ depiction in Fig. 2a to better convey the experimental design.
A recent nanoDESI report claiming cellular resolution annotated a couple dozen proteoforms (https://pubs.acs.org/doi/abs/10.1021/acs.analchem.2c04795).Given the large number of annotations in this report, there must be a comment on proteome coverage of MSI compared to LFQ.Were MSI annotations at 5-10% of LFQ annotations?10-20%?
Response: We appreciate the Reviewer for mentioning the recent advances in spatial proteoform annotation in the field.We ran a comparison between the MSI and LFQ annotated proteoforms using our algorithms.In particular, we overlayed the 618 MSI proteoforms with the 552 LFQ proteoforms passing 1% FDR and picked the ones within 3 Da of mass tolerance found that 249 of the MSI proteoforms were detected in LFQ, which corresponds to a 45% of proteoform overlap.The discrepancies in proteoform detection between MSI and LFQ is potentially due to the difference in linear dynamic range of the two approaches and the spatial heterogeneity of the sample.We have revised the paper accordingly on Line 202: "249 common proteoforms were found in both the imaging and the LFQ dataset, featuring a 45% overlap in proteome coverage."Methods state stages were moved at 3-5 µm/s for LFQ, but then specifically in LFQ section 4 µm/s with 2 spectra/s (512 ms transients on the Exploris) was stated.Given ~50 spectra per Figure 2, this means a maximum of 25 seconds of travel per 80 µm x 80 µm "pixel".After forming the junction on the tissue, it would take 20 s for the probe to move 80 µm, meaning only 40 acquisitions, or the actual sampled area was 80 µm by 100 µm.Assuming the length of the liquid microjunction was the same as the reported width (80 µm) and given it will take 20 s to move the probe 80 µm to a new area, this would be an extreme amount of oversampling on the tissue.The authors should comment on this.

Response:
We thank the Reviewer for thorough consideration and calculation for the LFQ experimental methodology.We would like to clarify that LFQ experiments was performed in exactly the same way as PiMS imaging experiment except the lower probe rastering rate (4 µm/s rather than 5 µm/s).During the experiment, the liquid bridge was moving continuously along the lateral direction to extract proteins while the mass spectrometer recorded a spectrum every 0.5 second.The dataset was reconstructed to generate data for each "sampled region" by binning adjacent ~50 spectra equivalent to 80 µm × 80 µm areas.The experiment was not performed by reestablishing a new liquid bridge and reinitiating the liquid extraction process for an adjacent 80 µm × 80 µm area and moving laterally for 20 seconds.
In LFQ experimental design, we intended to extract all proteoform signals from each sampled region without signal suppression caused by saturation.The figure below shows typical imaging profile at 5 µm/s (top) and LFQ profile at 4 µm/s (bottom).We did not observe signal saturation in the scans in imaging experiments, in which a higher protein concentration was analyzed (each unit volume of solvent extracts 20% more tissue area, and a 0.4 millisecond injection time was used rather than 0.2 millisecond in LFQ).Therefore, we believe that signal saturation rarely took place in LFQ experiments from these tissues.
Another important consideration for quantitation of each "sampled region" is to minimize the carryover of proteins from the previous "sampled region".For LFQ experiments for this set of cancer tissues, we optimized the rastering rate of the probe to make sure that proteins are maximally extracted in every 80 µm × 80 µm area with minimal carryover from the adjacent areas.In the figure shown above, we compared the temporal profile of total protein signal at a rastering rate of 5 µm/s (imaging experiments) and 4 µm/s (LFQ experiments).In contrast to even sampling profiles in imaging, LFQ profile shows many spike features with highest ion counts (at the tip of the spike) ~10% to 20% higher than the average profile in imaging experiment.These profiles correspond to an almost complete extraction of protein content within every 20 µm of lateral distance.In this regard, when protein signals within 80 µm distance are binned to construct a sampled region for LFQ quantitation, each region will contain minimal contribution from adjacent 80 µm area.Another reason we chose 80 µm as the lateral size of sample is based on the width of the liquid bridge (80 µm), which eliminates the difference between lateral or horizontal orientation and mimic the process in LCM, micropunch, and other microsampling approaches.
To address the reviewer's concern, we have included the above figure and explanation in the Methods section and as Supplementary Fig. 15, and we have also revised the conceptual depiction in Fig. 2a.

The authors state reproducibility for technical replicates. However, how do the authors account for the diminishing signal if a nanoDESI probe was allowed to sit on a surface for an extended period? Large number of acquisitions with blank signal could artificially inflate high abundance proteoforms and reduce low abundance proteoforms.
Response: We appreciate the focus here, as this is the first report of using individual ion MS to perform LFQ.The nano-DESI probe was constantly moving during the sampling process and did not park on a surface for an extended period of time.During the entire sampling process for LFQ, the MS injection time was constant, and the rastering rate was set to extensively sample proteins from each 80 µm × 80 µm sampled region in the tissue section.We reasoned that the reviewer was referring to the situation where the probe was allowed to park on the tissue for long time, and the initial MS scans would be saturated and dominated by high abundance proteoform signals.However, our experiments were performed at a rastering rate slightly lower than the imaging experiment, in which less protein signals were sampled per scan compared to imaging.Therefore, we do not anticipate artificial inflation of high abundance proteoforms due to the probe operation mode, and indeed, we observed similar spectral dynamic range in LFQ experiment compared to imaging as evidenced by the number of proteoforms detected in each experiment.

3)-3 AutoPiMS:
PiMS was defined as "Proteoform imaging Mass Spectrometry", and "AutoPiMS" has a strong connotation that is directly in-tandem with PiMS (i.e., MSI) but no more than one line of

AutoPiMS was demonstrated. It is unclear if one line scan needs to be done, or the entire MSI needs to be done to implement AutoPiMS?
Response: We agree with the Reviewer that AutoPiMS workflow is directly in-tandem with PiMS.However, we would like to clarify that PiMS is a term that describes the scalable technique that enables spatial profiling of proteoforms in tissue.PiMS data can be obtained for a single line or for a region containing multiple lines.In a similar sense, AutoPiMS can be performed with PiMS experiments of different scales including a single line or a region for targeted proteoform identification.

Given the amount of oversampling this would also impact subsequent "survey scans" or implementation in MSI line scans. Can the authors comment on how to take this from proof-ofconcept to actual implementation for more than one line scan?
Response: We agree with the Reviewer that oversampling is frequently observed during the survey line scan data acquisition.To obtain enough protein signal for MS 2 acquisition, we shifted the subsequent line scan by 20 µm as described in the main text (Line 76: "MS 2 fragmentation is then performed by running the PiMS probe across a fresh line parallel to the survey line scan but offset by ~20 µm").This allowed us to obtain precursor proteoform signals by accessing a 20 µm-wide fresh tissue region with highly similar proteoform spatial distributions compared to the adjacent survey line.In particular cases when precursor proteoform signal does not meet the abundance requirement for MS 2 experiments, we can further shift the probe by 50-75 µm.In the case of a survey imaging experiment with multiple parallel line scans, we reserve the space between the lines.MS 2 experiment will be performed in the staggered region containing fresh tissue area not scanned by the survey as illustrated in the figure below: We have revised the manuscript to further explain these concepts (Line 64): "AutoPiMS augments PiMS with a computational engine for unattended proteoform target selection and acquisition method generation, and is directly interfaced with high-throughput data processing and database search.AutoPiMS streamlines multiplexed on-tissue top down proteomics and can be readily interfaced with a variety of electrospray-based protein MS imaging modalities to extend proteome coverage in spatial proteomics, advancing the field of molecular histology." and Line 82: "We note that the first step in the AutoPiMS workflow is not limited to a single line scan but can also be applied to a PiMS imaging experiment with space between lines reserved for MS 2 data acquisition."

How automated is this technique? It appears manual inclusion of the lists in Xcalibur is required. ("The list is then imported into an instrument method in XCalibur...for MS2 data acquisition.")
Response: The data transfer steps and inclusion of the target list in XCalibur are not automated as the reviewer pointed out.All data acquisition, computational steps for target list generation and database searching are fully automated.We have revised the manuscript to clarify these points (Line 70): "AutoPiMS achieves unattended identification of proteoforms using a semiautomated spatially-aware, data-dependent acquisition strategy." and Line 81: "All steps aside from data transfer and MS 2 method setup are fully automated and can be customized manually as desired." AutoPiMS was completed at roughly half the speed of the PiMS.How long would the theoretical AutoPiMS of every line scan on the Exploris take?
Response: We performed AutoPiMS survey scan at 2-4 µm/s, and the line scan took 35-50 minutes to complete.In the line scan described in Supplementary Fig. 6, the survey line scan took 40 minutes to cover a 9.6 mm line region at 4 µm/s rastering rate.The corresponding MS 2 experiment took identical time as the survey scan.Considering the computational steps (e.g., MS 2 target list curation, datafile handling and transferring, database searching) and manual operation to shift the sample between the survey and the MS 2 lines, an entire AutoPiMS experiment takes 2-3 hours to finish.We have added this information in Methods section (section: "General schematic AutoPiMS workflow") to address the comment.Moreover, we have also added a table in "PiMS ion source and sampling conditions" section to clarify the probe rastering rates we used for different experimental workflows:

2-4
Information on all the timings should be included to allow for some practical estimates on the overall experimental time.Using the parameters provided, simple calculation suggests >10 hrs.
How long can the tissue be at ambient temperature before there are noticeable changes due to oxidation or truncations or cleavages from proteases?

Response:
We have compared the level of oxidation at 0 and 24 hours acquired on the same tissue section using Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as a reporter.As shown in the figure below, canonical GAPDH proteoform (35.92 kDa, left) is oxidized at C152 by ambient oxygen evidenced by a mass shift of 32 Da corresponding to the addition of two oxygen atoms (35.95 kDa, right).The oxidated GAPDH was at 10% level of the canonical GAPDH after 24 hours of ambient air exposure.This proves that within the time frame of one AutoPiMS experiment (2-3 hours), the effect of tissue oxidation on the data quality was negligible.
Regarding protein truncation by proteases, we have applied successive ethanol washes to the tissue section prior to PiMS and AutoPiMS experiments, which fixes and precipitates the proteins (described in detail in Methods section: "Tissue/sample preparation").With the ethanol wash, protease activities should be deactivated.
From the Figure 2, it is unclear if AutoPiMS was performed on the same area of tissue that had LFQ completed.
Response: AutoPiMS was performed along the dashed line region in Figure 2. We have revised the manuscript to clarify this (Line 210): "A survey line scan followed by an adjacent MS 2 scan were performed along the dashed line in Fig. 2a next to the LFQ-profiled tumor and stromal regions."4) Abstract: "automated molecular histology approach" is cryptic and uninformative.This statement suggests the approach yielded images of 1000 proteoforms, which is misleading.
Rephrase and clearly explain the intricacies of the approach resulting in different depth of coverage associated with different workflows.

Response:
We agree and therefore have removed the cryptic phrase and instead used a clear phrase to address the concern.Response: We thank the Reviewer for suggesting these techniques, and we would like to include these recent advances in the field in the introduction and references 19).However, we point out that MALDI predominantly generates singly-charged ions that significantly limits its ability to interface with top-down fragmentation due to the lack of sequence coverage in charged fragments.As the reviewer mentioned, novel MS 2 data acquisition approaches coupled to spatial and single-cell proteomics have made huge strides in the field bottom-up proteomics, where proteins are digested into peptides and are subjected to chromatographic/ion mobility separation prior to MS/MS fragmentation.However, whole proteoform information are typically not preserved in protein digestion steps in these workflows.

Response:
We have revised the sentence as (Line 45): "When using electrospray ionizationbased methods for proteoform desorption from tissues, each proteoform generates a distribution of charge states, resulting in congested spectra in the mass-to-charge (m/z) domain."Moreover, we introduced nano-DESI in the second paragraph (Line 52).
7) Line 50: This is "nanoDESI" and not "DESI" (and these common abbreviations should be introduced Response: We reasoned that the Reviewer was referring to Supplementary Fig. 5 in this comment.We note that the m/z spectra were obtained by reconstruction of the mass-domain spectra using absolute ion counts from detected proteoform in the mass domain, which does not necessarily show the true signal to noise of the detection.As described in the flagship I 2 MS publication (Kafader et al., Nat. Methods, 2020), I 2 MS is a single ion approach that every ion used to construct the spectrum has been stringently evaluated based on a variety of metrics (e.g., STORI slope, R 2 value, time of birth, time of survival).As a result, conventional random noise does not typically appear in the mass-domain spectrum in I 2 MS.To avoid confusion and use this figure as an opportunity to demonstrate that the target proteoform is the dominant species in AutoPiMS selected m/z isolation windows from a complex proteoform mixture, we have replaced Supplementary Fig. 5 by showing the absolute ion counts of all the proteoforms isolated in the selected m/z isolation windows in each case: In each panel, proteoforms are listed from high to low in ion count.The red numbers in each panel indicate the absolute ion count of that proteoform in the isolation window.As we could see in this figure, the target proteoforms we intended to isolate were present with substantially higher ion count than other co-isolated proteoforms.AutoPiMS as the only focus in Fig. 1.We hope the Reviewer would appreciate the importance of these two figure panels while reading the revised manuscript.
19) Figure 2a, was AutoPiMS applied only along the vertical dashed line (and not implemented for the PiMS region)?
Response: Yes, the Reviewer is correct.We have also revised Fig. 2a and made the clarification in the manuscript (Line 210): "A survey line scan followed by an adjacent MS 2 scan were performed along the dashed line in Fig. 2a.next to the LFQ-profiled tumor and stromal regions."

20) How can one be certain that a monomethylation is present solely on Arg68 if only one
AutoPiMS vertical line scan was applied?
Response: We agree with the Reviewer that strictly in terms of the supporting fragmentation in Fig. 2f, the monomethylation may not be localized to a single residue.Furthermore, in the stretch of six amino acid residues to which the monomethylation is localized, all six residues are viable candidates for monomethylation.However, in the literature and UniProt database PTM annotation (https://www.uniprot.org/uniprotkb/P50238/entry#ptm_processing,screenshot below), arginine residues are more likely methylated compared to the other candidates, and the arginine residue is the only candidate in that region which can support dimethylation (Clarke et al., Trends in Biochemical Sciences, 2013), which was also detected and coarsely localized to the region.Therefore, the monomethylation can be confidently assigned to the arginine as the dominant site despite not being localized to single-residue precision through fragmentation data.
To clarify this point, we have revised the manuscript accordingly (Line 225): "The MS 2 data support the placement of the mono-and dimethylation on Arg68 as a major modification site".
21) Terminology is also confusing, "pixels" would suggest that the LFQ experiment could create images.If so, why not include these images?
Response: We have revised the terminology "pixels" as "sampled regions" in the sections describing LFQ experiment.As explained in Comment 3)-2, the goal of the LFQ experiment is to sample hundreds of samples containing from the histopathological region for quantitative interpretation of the proteoform detection level.Imaging dataset is best suited for reflecting the spatial abundance variations within the same region and does not align well with the experimental design of the LFQ experiments.
22) It is challenging to gauge small differences in the images with Jet or other color schemes, consider changing to viridis or ciridis.Overlaying of Jet with a red-scale image makes figure 2f hard to read.
Response: We agree with the Reviewer that the overlayed red image with a jet color scale causes confusions.We have changed the overlayed image to a two-channel image in Fig. 2f with VIM shown in blue and CRIP1 Arg68Me0 in red as follows: 23) Figure 2 and related text: Ideally, the report should have included a validation of selected proteins (and/or PTMs) using complementary techniques.
Response: We agree with the Reviewer that although we showed quantitative analysis, intact mass annotation and top-down tandem MS identification for each proteoform with an image, we did not include orthogonal validation in this manuscript, such as immunohistochemistry.
However, we were able to include quantitation results of 8 proteins (Supplementary Fig. 11) we discussed in Figure 2 from a study of ovarian cancer tissues we have published previously (Hunt et al., iScience, 2021).In this previous study, tumor and stromal regions were sampled by laser capture microdissection and analyzed using TMT-LC-MS bottom-up proteomics workflow.5 of the common proteins show consistent enrichment in tumor or stroma in these two studies.We have also revised the manuscript accordingly (Line 234): "This explains the highly variable CRIP1 abundances in our previous bottom-up proteomics report on tumor-or stroma-enriched HGSOC samples (Supplementary Fig. 11)." 24) Just as the main body of the manuscript, the methods were hard to digest with three instruments with different settings used for different purposes.It is challenging to find the information with current organization (for example, identify parameters specific to the Q McGee et al. describes an exciting and novel approach to characterize proteoforms in localized tissue regions addressing an important but understudied area of spatial biology.The authors present an expansion of their recent workflow for proteoform informed imaging (PiMS).Previously, a Q Exactive Plus with single ion (I2MS) detection schemes was employed for mass spectrometry imaging (MSI), coined PiMS, and ion images of proteoforms up to ~70 kDa were acquired via nanospray desorption electrospray ionization (nanoDESI) MSI at 100 µm by 150 µm resolution (https://www.science.org/doi/10.1126/sciadv.abp9929).This permitted annotation of 42% of the 400 proteoforms detected by nanoDESI via external human kidney experimental databases and conformation of over a dozen proteoforms with in-situ fragmentation by HCD.Herein, the authors boosted the spatial resolution of PiMS, this time on an Exploris 480, to 20 µm (i.e., near cellular) resolution within the line scan and 80 µm lateral step size.They use several different instruments to complete one of the three workflows: PiMS and the first half of the manuscript (before Line 151) on the technical development of AutoPiMS workflow, and pivot into spatial biology study (Fig. 2) using multifaceted PiMS-derived techniques including imaging, LFQ, and AutoPiMS.Detailed changes to the manuscript are listed below in point-by-point responses to Reviewer's comments.3)-1 Mass spectrometry imaging: MSI: The PiMS (i.e., MSI experiment) was accomplished on an Exploris 480 (high-field Orbitrap) with 512 ms transient acquisitions.The authors do not make it explicitly clear if I 2 MS detection is enabled on the Exploris 480, but one would assume yes based on the recent report and PiMS acronym being somewhat synonymous to I 2 MS-MSI due to previous implementation on the Q Exactive Plus (low-field Orbitrap).The methods are hard to follow with 3 Orbitraps being used for multiple workflows.The authors should consider breaking methods into 3 different sections (corresponding to 3 different workflows) for clarity.

Supplementary Fig. 2, and Supplementary Table 1), of
We have removed the scan rate description in the main text and removed the introduction of abbreviation of Hertz to avoid confusion as suggested by the Reviewer.
Response:We have reorganized the Methods section and reformulated the main text to describe all the different workflows we developed in this work, including AutoPiMS, PiMS imaging, and label-free quantitation.In particular, we have provided detailed responses to this question in Reviewer question 3).10)Lines 101-109: 87 of 113 proteoforms, yet favorable windows only for 25? Are these 25 a subset of larger proteoforms within the dataset?Response: The Reviewer is correct.We have mentioned in Line 112 that "We obtained 113 proteoform masses at >1% relative abundance ranging from 4-67 kDa from a single 40-minute survey line scan(Methods, 18) Figures are overly complicated and therefore difficult to follow and connect toexplanations and conclusions provided in the text.Some of this material can be moved to SI to make the overall flow of the experiment clearer and connect better to the text.For example, We appreciate the Reviewer's suggestions about the figure complexity.However, we reasoned that these two panels demonstrate critical performance metrics of the novel data acquisition algorithms embedded in AutoPiMS and should stay in the main text.With the great suggestions from the Reviewer, we have put in substantial effort in the improving the flow of the revised manuscript to avoid confusions from multiple workflows and datatypes and keep Response: